{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "742c00de-ce61-4234-8c35-f196cb5bfeb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db66a0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(filepath):\n",
    "    data = pd.read_excel(filepath)\n",
    "    # 数据预处理，确保列名匹配\n",
    "    data.columns = ['温度，oC', '频率，Hz', '磁芯损耗，w/m3', '励磁波形'] + [str(j) for j in range(1024)]\n",
    "    data['磁通密度峰值'] = data[[str(j) for j in range(1024)]].max(axis=1)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a518cdbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulated_annealing(data, initial_temp, cooling_rate, max_iterations):\n",
    "    current_temp = initial_temp\n",
    "    current_state = {\n",
    "        '温度': random.choice(data['温度，oC'].unique()),\n",
    "        '频率': random.choice(data['频率，Hz'].unique()),\n",
    "        '磁通密度峰值': np.median(data['磁通密度峰值'])\n",
    "    }\n",
    "    current_cost = evaluate_cost(current_state, data)\n",
    "    \n",
    "    costs = []\n",
    "    temperatures = []\n",
    "\n",
    "    for i in range(max_iterations):\n",
    "        next_state = get_neighbor(current_state, data)\n",
    "        next_cost = evaluate_cost(next_state, data)\n",
    "\n",
    "        cost_diff = next_cost - current_cost\n",
    "\n",
    "        if cost_diff < 0 or np.exp(-cost_diff / current_temp) > random.random():\n",
    "            current_state, current_cost = next_state, next_cost\n",
    "\n",
    "        current_temp *= cooling_rate\n",
    "        \n",
    "        # 记录每次迭代的成本和温度\n",
    "        costs.append(current_cost)\n",
    "        temperatures.append(current_temp)\n",
    "\n",
    "        if i % 100 == 0:\n",
    "            print(f\"Iteration {i}: Temp = {current_temp:.2f}, Cost = {current_cost}\")\n",
    "\n",
    "    return current_state, costs, temperatures\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9fbea24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_cost(state, data):\n",
    "    loss = data[(data['温度，oC'] == state['温度']) & (data['频率，Hz'] == state['频率'])]['磁芯损耗，w/m3'].mean()\n",
    "    energy = state['频率'] * state['磁通密度峰值']\n",
    "    return loss - energy  # 损耗越小越好，能量越大越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9644671",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_neighbor(state, data):\n",
    "    new_state = state.copy()\n",
    "    if random.random() > 0.5:\n",
    "        new_state['温度'] = random.choice(data['温度，oC'].unique())\n",
    "    else:\n",
    "        new_state['频率'] = random.choice(data['频率，Hz'].unique())\n",
    "    return new_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba547f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_optimization_history(costs, temperatures):\n",
    "    fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "    # 成本变化曲线\n",
    "    color = 'tab:red'\n",
    "    ax1.set_xlabel('迭代次数')\n",
    "    ax1.set_ylabel('成本', color=color)\n",
    "    ax1.plot(costs, color=color)\n",
    "    ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "    # 温度变化曲线\n",
    "    ax2 = ax1.twinx()\n",
    "    color = 'tab:blue'\n",
    "    ax2.set_ylabel('温度', color=color)\n",
    "    ax2.plot(temperatures, color=color)\n",
    "    ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "    plt.title('优化历史：迭代过程中的成本与温度变化')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8128e6ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization Config\n",
    "# 设置字体路径,注意这里使用你系统中 Noto Sans CJK SC 的实际路径\n",
    "font_path = '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc'\n",
    "# load and config fonts\n",
    "prop = fm.FontProperties(fname=font_path)\n",
    "plt.rcParams['font.family'] = prop.get_name()\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48781a08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0: Temp = 9900.00, Cost = 13021.922492510002\n",
      "Iteration 100: Temp = 3623.72, Cost = -9600.140221615\n",
      "Iteration 200: Temp = 1326.40, Cost = -7260.764136124999\n",
      "Iteration 300: Temp = 485.50, Cost = -13625.024322829999\n",
      "Iteration 400: Temp = 177.71, Cost = -13625.024322829999\n",
      "Iteration 500: Temp = 65.05, Cost = -13625.024322829999\n",
      "Iteration 600: Temp = 23.81, Cost = -13625.024322829999\n",
      "Iteration 700: Temp = 8.72, Cost = -13625.024322829999\n",
      "Iteration 800: Temp = 3.19, Cost = -13625.024322829999\n",
      "Iteration 900: Temp = 1.17, Cost = -13625.024322829999\n",
      "Optimized State: {'温度': 70, '频率': 281860, '磁通密度峰值': 0.061334190499999997}\n"
     ]
    }
   ],
   "source": [
    "file_path = '附件一（训练集）.xlsx'\n",
    "data = load_data(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff93ae1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_temp = 10000\n",
    "cooling_rate = 0.99\n",
    "max_iterations = 1000\n",
    "\n",
    "# 接收返回的最优状态以及成本和温度历史\n",
    "optimal_state, costs, temperatures = simulated_annealing(data, initial_temp, cooling_rate, max_iterations)\n",
    "print(\"Optimized State:\", optimal_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ef33335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用返回的历史数据绘制图形\n",
    "plot_optimization_history(costs, temperatures)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mathmodal",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
